Everything You Ever Wanted to Know About Bitcoin Mixers (But Were Afraid to Ask)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The lack of fungibility in Bitcoin has forced its userbase to seek out tools that can heighten their anonymity. Third-party Bitcoin mixers use obfuscation techniques to protect participants from blockchain transaction analysis. In recent years, various centralized and decentralized Bitcoin mixing methods were proposed in academic literature (e.g., CoinJoin, CoinShuffle). Although these methods strive to create a threat-free environment for users to preserve their anonymity, public Bitcoin mixers continue to be associated with theft and poor implementation. This paper explores the public Bitcoin mixer ecosystem to identify if today’s mixing services have adopted academia’s proposed solutions. We perform real-world interactions with publicly available mixers to analyze both implementation and resistance to common threats in the mixing landscape. We present data from 21 publicly available mixing services on the deep web and clearnet. Our results highlight a clear gap between public and proposed Bitcoin mixers in both implementation and security. We find that the majority of key security features proposed by academia are not deployed in any public Bitcoin mixers that are trusted most by Bitcoin users. Today’s mixing services focus on presenting users with a false sense of control to gain their trust rather than employing secure mixing techniques.

Original languageEnglish (US)
Title of host publicationFinancial Cryptography and Data Security - 25th International Conference, FC 2021, Revised Selected Papers
EditorsNikita Borisov, Claudia Diaz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages117-146
Number of pages30
ISBN (Print)9783662643211
DOIs
StatePublished - 2021
Externally publishedYes
Event25th International Conference on Financial Cryptography and Data Security, FC 2021 - Virtual, Online
Duration: Mar 1 2021Mar 5 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12674 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Financial Cryptography and Data Security, FC 2021
CityVirtual, Online
Period3/1/213/5/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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